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A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

Liu Z, Liu Y, Shan H, Cai B, Huang Q - PLoS ONE (2015)

Bottom Line: Therefore, diagnostic accuracy and capacity can be improved.Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only.It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

View Article: PubMed Central - PubMed

Affiliation: College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao, 266580, China.

ABSTRACT
This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

No MeSH data available.


A simple Bayesian network.
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pone.0125703.g002: A simple Bayesian network.

Mentions: A Bayesian network contains two elements, namely structure and parameters. An example shown in Fig 2 is used to illustrate the basic idea of Bayesian networks. In Fig 2, the nodes (X1, X2, X3, X4) represent random variables and arcs means dependence relationships among them. Each arc starts from a parent node and ends at a child node. Pa(X) represents the parent nodes of node X, therefore, Pa(X2) = {X1}, Pa(X3) = {X1}, Pa(X4) = {X2, X3}. X1 is the root node, because it has no input arcs. Each node has two states: state0 and state1. Root nodes have prior probabilities. Each child node has conditional probabilities based on the combination of states of its parent nodes.


A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

Liu Z, Liu Y, Shan H, Cai B, Huang Q - PLoS ONE (2015)

A simple Bayesian network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4418566&req=5

pone.0125703.g002: A simple Bayesian network.
Mentions: A Bayesian network contains two elements, namely structure and parameters. An example shown in Fig 2 is used to illustrate the basic idea of Bayesian networks. In Fig 2, the nodes (X1, X2, X3, X4) represent random variables and arcs means dependence relationships among them. Each arc starts from a parent node and ends at a child node. Pa(X) represents the parent nodes of node X, therefore, Pa(X2) = {X1}, Pa(X3) = {X1}, Pa(X4) = {X2, X3}. X1 is the root node, because it has no input arcs. Each node has two states: state0 and state1. Root nodes have prior probabilities. Each child node has conditional probabilities based on the combination of states of its parent nodes.

Bottom Line: Therefore, diagnostic accuracy and capacity can be improved.Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only.It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

View Article: PubMed Central - PubMed

Affiliation: College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao, 266580, China.

ABSTRACT
This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

No MeSH data available.